Weiming ZHU
Prof. Weiming ZHU
Innovation and Information Management
Associate Professor

3910 3094

KK 1305

Academic & Professional Qualification
  • Ph.D, Operations Management, R.H Smith School of Business, University of Maryland, 2016
  • B.Sc., Physics, HKUST, 2011
Biography

Weiming Zhu is an Associate Professor in Innovation and Information Management at HKU Business School. Weiming obtained his bachelor’s degree in Physics from HKUST and Ph.D. in Operations Management from the Robert H. Smith School of Business at University of Maryland. Prior to joining the University of Hong Kong, he was an Associate Professor in IESE’s Department of Production, Technology and Operations Management. Weiming has also been a visiting professor in the Institute for Data, Systems, and Society (IDSS) at Massachusetts Institute of Technology and Kellogg School of Management at Northwestern University.

Weiming’s current research includes assortment display and agent behavior in the sharing economy, the effectiveness of financing schemes in supply chains, and the role of retail distribution methods on channel development. His work has been recognized in M&SOM, POMS, Service Science and CSAMSE best paper award competitions.

Research Interest
  • Empirical Operations Management
  • Economics of Operations Management
  • The Sharing Economy
  • Operations – Finance Interface
Selected Publications
  • Estimating and Exploiting the Impact of Photo Layout: A Structural Approach (with Hanwei Li, David Simchi-Levi and Michelle Wu). Management Science, 69(9), 4973-5693, 2023.
  • Buyer Intermediation in Supplier Finance (with Tunay I. Tunca). Management Science, 64 (12), 5461 – 5959. doi:10.1287/mnsc.2017.2863.
  • The Alibaba Effect: Spatial Consumption Inequality and Welfare Gains from e-Commerce (with Jingting Fan, Lixin Tang and Ben Zou). Journal of International Economics,  114, 203 – 220. doi:10.1016/j.jinteco.2018.07.002.
  • The Incentive Game under Target Effects in Ridesharing: A Structural Econometric Analysis (with Xirong Chen, Zheng Li and Liu Ming). Manufacturing & Service Operations Management, 24 (2), 972-992. doi:10.1287/msom.2021.1002.
  • Improving Channel Efficiency through Financial Guarantees by Large Supply Chain Participants (with Tunay I. Tunca), 2017. Foundations and Trends® in Technology, Information and Operations Management 10, no. 3-4 (2017), pp.289-304.
Awards and Honours
  • Second Prize, 2021 INFORMS Service Science Best Paper Award Competition
  • Winner, MSOM iFORM SIG Best Paper Award, 2019
  • Honorable Mention, Chinese Scholars Association in Management Science and Engineering (CSAMSE), Best Paper Award, July 2017
  • Finalist, MSOM Student Paper Competition, November 2016
  • First Prize, POMS Supply Chain Student Paper Competition, May 2016
  • Honorable Mention, Chinese Scholars Association in Management Science and Engineering (CSAMSE), Best Paper Award, July 2015
  • Alibaba Running Water Project funding, The Alibaba Effect: Spatial Consumption Inequality and Welfare Gains from e-Commerce, April 2015
Service to the University/ Community
  • Referee for Management Science, Manufacturing & Service Operations Management
Recent Publications
Estimating and Exploiting the Impact of Photo Layout: A Structural Approach

Host-generated property images as a visual channel reveal substantial information about properties. Selecting proper images to display can lead to higher demand and increased rental revenue. In this paper, we define, estimate, and optimize the impacts of Airbnb photos on customers’ renting decisions. We apply ResNet-50, a convolutional neural network model, to build two separate, supervised learning models to evaluate the image quality and room types posted by Airbnb hosts. Then, we characterize the overall impacts of photo layout by the room type featured in the photo, photo quality, and order of display on the listings’ web pages. To address two estimation challenges in the Airbnb setting, namely, censored demand and changing consideration sets, we propose a novel pairwise comparison model that utilizes customers’ booking sequence data to consistently estimate the impact of photo layout on customers’ renting decisions. Our estimation results suggest that the cover image has a significantly larger impact than noncover photos and a high-quality bedroom cover image leads to the largest increase in demand. Furthermore, we build a nonlinear integer programming optimization problem and develop an algorithm to determine the optimal photo layout. Our counterfactual analysis suggests that a listing’s unilateral adoption of optimal photo layout leads to 11.0% more bookings on average. Moreover, depending on the neighborhood and market size, when listings simultaneously switch to the optimal photo layout, they get booked for two to five additional days in a year on average, which boosts revenue by $500 to $1,100.

Get to know Dr. Weiming Zhu